SepsisPhase 2NCT03734484
Gram Type Infection-Specific Sepsis Identification Using Machine Learning
A Phase 2 study testing InSight in people with Sepsis and Severe Sepsis, run by Dascena.
Trial summary
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Sepsis, Severe Sepsis, Septic Shock
- What is being tested?
- InSight
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- 1 May 2022to 1 Mar 2023
- Phase
- Phase 2
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- All adults above age 18 who are a member of one of the three subpopulations studied in this trial (patients with Gram-positive infection, patients with Gram-negative infection, and patients with mixed Gram-positive and Gram-negative infection) are eligible to participate in the study.
Who cannot join
- Under age 18
- No record of Gram infection
About this study
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.
- Conditions
- Sepsis, Severe Sepsis, Septic Shock
- Being tested
- InSight
- Keywords
- Dascena, Gram infection, antibiotic administration, machine learning, algorithm, diagnostic
- Medical subject headings
- Sepsis, Shock, Septic, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
InSightDiagnostic test
The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between various Gram-type infections.
| Study group | Role | What they receive |
|---|---|---|
| Gram type infection-specific algorithm | Experimental | The experimental arm will involve patients monitored by the Gram type infection-customized version of InSight. Receives: Diagnostic Test: InSight |
| Standard treatment protocol | No intervention | The control arm will involve patients treated with the regular diagnosis and treatment protocol for gram-type infection, where fluid cultures are run to determine infection type. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in time to antibiotic administration
Change in time period between diagnosis of Gram infection and administration of antibiotics to treat infection
Measured: Through study completion, an average of 8 months
Secondary outcomes (2)
Change in administration of unnecessary antibiotics
Changes in amount of secondary antibiotics administered
Measured: Through study completion, an average of 8 months
Change in administration of unnecessary antibiotics
Changes in total hours spent on antibiotics
Measured: Through study completion, an average of 8 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Nov 2018
- Study start1 May 2022 (estimated)
- Primary completion30 Nov 2022 (estimated)
- Study completion1 Mar 2023 (estimated)
Why it stopped: study was not started.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for SepsisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 2
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Investigator
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Dascena
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Ritankar Das, MSc, Principal investigator, Dascena
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03734484
- Sponsor's protocol ID
- 01072019
- Status last verified
- Sep 2021
- First submitted
- 1 Nov 2018
- Last updated
- 23 Sep 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.
PubMed 27699003Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
PubMed 29435343Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
PubMed 29374661
Study at a glance
From the registry record, last updated by the sponsor on 23 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Gram Type Infection-Specific Sepsis Identification Using Machine Learning
- ClinicalTrials.gov ID
- NCT03734484
- Acronym / sponsor ID
- 01072019
- Conditions
- Sepsis, Severe Sepsis, Septic Shock
- Keywords
- Dascena, Gram infection, antibiotic administration, machine learning, algorithm, diagnostic
- Phase
- Phase 2
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: study was not started.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- DascenaIndustry (pharma, biotech, devices)
- Study start
- 1 May 2022 (estimated)
- Primary completion
- 30 Nov 2022 (estimated)
- Study completion
- 1 Mar 2023 (estimated)
- First posted
- 8 Nov 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, care provider, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Gram type infection-specific algorithm | Experimental | Diagnostic Test: InSightThe experimental arm will involve patients monitored by the Gram type infection-customized version of InSight. |
| Standard treatment protocol | No intervention | –The control arm will involve patients treated with the regular diagnosis and treatment protocol for gram-type infection, where fluid cultures are run to determine infection type. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All adults above age 18 who are a member of one of the three subpopulations studied in this trial (patients with Gram-positive infection, patients with Gram-negative infection, and patients with mixed Gram-positive and Gram-negative infection) are eligible to participate in the study.
Key exclusion criteria (2 listed)
- Under age 18
- No record of Gram infection
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| InSightThe InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between various Gram-type infections. | Diagnostic test | Gram type infection-specific algorithm |
Primary outcome
Change in time to antibiotic administration
Time frame: Through study completion, an average of 8 months
Change in time period between diagnosis of Gram infection and administration of antibiotics to treat infection
2 secondary outcomes
Change in administration of unnecessary antibiotics
Time frame: Through study completion, an average of 8 months
Change in administration of unnecessary antibiotics
Time frame: Through study completion, an average of 8 months
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2021.
- Background: PubMed 27699003Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.
- Background: PubMed 29435343Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
- Background: PubMed 29374661Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ritankar Das, MSc principal investigator
Dascena
Questions about this trial
What is this clinical trial studying?
A Phase 2 study testing InSight in people with Sepsis and Severe Sepsis, run by Dascena.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 23 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 2 studies give the treatment to a larger group to see whether it has the intended effect and to keep checking safety.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in time to antibiotic administration (time frame: Through study completion, an average of 8 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03734484, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Sep 2021.